Change Bursts as Defect Predictors - Nagappan, Nachi; Zeller, Andreas; Zimmermann, Tom; Herzig, Kim; Murphy, Brendan (Proceedings of the 21st IEEE International Symposium on Software Reliability Engineering (ISSRE), 2010)
In software development, every change induces a risk. What happens if code changes again and again in some period of time? In an empirical study on Windows Vista, we found that the features of such change bursts have the highest predictive power for defect-prone components. With precision and recall values well above 90%, change bursts significantly improve upon earlier predictors such as complexity metrics, code churn, or organizational structure. As they only rely on version history and a controlled change process, change bursts are straight-forward to detect and deploy.